"""
TABLE_DATA = []
TABLE_DATA.append(
['Data set', 'slope', 'intercept', 'r', 'p-value'])
for s in range(Globals.n_sets):
TABLE_DATA.append([
str(Globals.dataset_names[s]),
str(round(lt_params[s][0], 7)),
str(round(lt_params[s][1], 5)),
str(round(lt_params[s][2], 5)),
str(lt_params[s][3])
])
TABLE_DATA = tuple(TABLE_DATA)
table_instance = SingleTable(TABLE_DATA, "Linear trend fit")
table_instance.justify_column = 'right'
print ""
print(table_instance.table)
except:
#if terminaltables is not installed
for s in range(Globals.n_sets):
print ""
print_message(
'\t/' + str(s) + " Data set:" +
str(Globals.dataset_names[s]), 3, 32)
print "\tslope:", lt_params[s][0]
print "\tintercept:", lt_params[s][1]
print "\tr:", lt_params[s][2]
print "\tp-value:", lt_params[s][3]
def load_multiset_data():
"""
"""
#multiset
Globals.n_sets = 0
print_message("\nReading multiset data\n", index=4, color=34)
#count data files passed as CLI arguments
Globals.times, Globals.rvs, Globals.rv_errs = [],[],[]
Globals.dataset_names, DATA = [], []
Globals.mean_err = []
try:
from terminaltables import AsciiTable, DoubleTable, SingleTable
ttable = True
except:
ttable = False
s = 0
for arg in sys.argv[1:]:
if arg[0] != '-':
#read data
try:
in_data = mgls_io.read_file(arg, ' ')
if not ttable: print_message("\t" + str(s) + "/ Read data file " + arg, index=3, color=32)
except IOError:
print_message("\tData file could not be read", 5, 31)
sys.exit()
#assign data vectors
time, rv, rv_err = mgls_io.get_data_vectors(in_data, Globals.col)
if Globals.km2m:
rv *= 1000.0
rv_err *= 1000.0
Globals.dataset_names.append( str(s) + '/ ' + arg.split('/')[-1]) #file name. Not full path
Globals.times.append(time)
mean_rv = np.mean(rv)
mean_rv_err = np.mean(rv_err)
Globals.mean_err.append(mean_rv_err)
Globals.rvs.append(rv)
Globals.rv_errs.append(rv_err)
inv_sigma2 = 1.0/(rv_err**2)
logL_0 = -0.5*(np.sum(((rv-mean_rv)**2)*inv_sigma2 + np.log(2.0*np.pi) + np.log(1.0/inv_sigma2)) )
Globals.n_sets += 1
#print info
summ = 0.0
separation = []
for i in range(len(time)-1):
summ += time[i+1] - time[i]
separation.append(time[i+1] - time[i])
if ttable:
DATA.append([len(time), time[-1] - time[0], summ/len(time), logL_0])
else:
if not Globals.inhibit_msg:
print "\t-----------------Summary of data--------------------"
print "\tData points:", len(time)
print "\tTime span:", time[-1] - time[0]
print "\tMean sep.", summ/len(time)
print "\tlogL null model (data, no jitter):", logL_0
print "\t----------------------------------------------------"
#count dataset
s += 1
#print ttable (if appliable)
if ttable:
TABLE_DATA = []
TABLE_DATA.append(['Data set name (full path not shown)', 'Data points', 'Timespan', 'Mean sep.', 'logL (data)'])
for s in range(Globals.n_sets):
TABLE_DATA.append([str(Globals.dataset_names[s]), str(DATA[s][0]), str(round(DATA[s][1],5)), str(round(DATA[s][2],5)), str(round(DATA[s][3],5))])
TABLE_DATA = tuple(TABLE_DATA)
table_instance = SingleTable(TABLE_DATA, "Dataset summary")
table_instance.justify_column = 'right'
print(table_instance.table)
Globals.len_sets = [len(Globals.times[i]) for i in range(len(Globals.times))]
Globals.times_seq, Globals.rvs_seq, Globals.rv_errs_seq = [], [], []
for i in range(len(Globals.times)):
Globals.times_seq.extend(Globals.times[i])
Globals.rvs_seq.extend(Globals.rvs[i])
Globals.rv_errs_seq.extend(Globals.rv_errs[i])
Globals.times_seq = np.array(Globals.times_seq)
Globals.rvs_seq = np.array(Globals.rvs_seq)
Globals.rv_errs_seq = np.array(Globals.rv_errs_seq)
